Bibliographic record
Abstract
Abstract This chapter starts with a paradox: ‘development’ is working while ‘development policy’ is not. On the one hand, the last quarter century has witnessed a tremendous and historically unprecedented improvement in the material conditions of hundreds of millions of people living in some of the poorest parts of the world. On the other hand, ‘development policy’ as it is commonly understood and advocated by multilateral organizations, aid agencies, Northern academics, and Northern-trained technocrats has largely failed to live up to its promise. For evidence on the former point, we can turn to Asia. For evidence on the latter, we can look at Latin America and Africa. One conclusion one could take from this is that our ability as economists to design and recommend growth strategies is extremely limited. It is argued that we can do better than adopt this kind of nihilistic attitude towards policy advice. If the original Washington Consensus erred in being too detailed and specific, and in assuming that the same set of policies work the same everywhere, policy nihilism goes too far in undervaluing the benefit of economic reasoning. The chapter outlines a way of thinking about growth strategies that avoids these two extremes. This approach consists of three elements. First, we need to undertake a diagnostic analysis to figure out where the most significant constraints on economic growth are. Second, we need creative and imaginative policy design to target the identified constraints appropriately. Third, we need to institutionalize the process of diagnosis and policy response to ensure that the economy remains dynamic and growth does not peter out. Each of these elements is discussed.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.012 | 0.026 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.003 | 0.014 |
| Scholarly communication | 0.010 | 0.010 |
| Open science | 0.004 | 0.005 |
| Research integrity | 0.005 | 0.006 |
| Insufficient payload (model declined to judge) | 0.026 | 0.004 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".